The present invention relates to the field of detecting leaks in pipes. More specifically, the present invention relates to apparatus and methods for detecting leaks which require transmission of data recorded at the pipe to a remote processor.
Fluid flowing through a pipe constantly generates an acoustic signal which propagates along the walls of the pipe and the through the fluid itself. If there is a leak in the pipe, the escaping fluid and the fluid passing over the leak, also generates an acoustic signal. Therefore, a leak can be detected by listening for such an acoustic signal.
There are known methods for accurately determining the position of a leak. For example, a popular method is to use a xe2x80x98leak noise correlatorxe2x80x99. This comprises a plurality of fixed sensors which are located at intervals along the pipe. If a leak occurs in a section of pipe between two sensors, both of the sensors detect the acoustic signal from the leak. The acoustic signal will propagate from the leak at the speed of sound in the pipe. Therefore, the time at which the two sensors detect the leak signal will depend on their relative distances from the leak. Comparing the arrival time of the leak signal at the two fixed sensors allows the position of the leak to be determined.
Generally, acoustic signals measured by the two sensors are transmitted away from the sensors to a processing unit for comparison. The acoustic signal from the leak is buried in the background acoustic signal from the pipe and is hard to extract, especially when the leak is small. Therefore, it is advantageous to process a large amount of data from the sensors in order to detect and pinpoint a leak. This causes a problem as there is a needed to transmit a large amount of data from the sensors to the remote processor.
For example, a typical leak noise correlator will require acoustic data sampling at a rate of 10 kHz. If the signal is digitised using a 16 bit analogue to digital converter, it will be necessary to transmit at least 160,000 bits per second of information for each sensor, if the pipe is to be monitored in real time. This transmission rate is beyond the capabilities of current, readily available, radio modem technology.
A lower sampling frequency could be used, but this results in a higher uncertainty in the predicted position of the leak. The data could be compressed using a standard compression technique, such as logarithmic compression. However, standard compression techniques create unacceptable loss in the resolution of the signal, which makes detection of a weak leak signal virtually impossible. Spread spectrum radio modems can transmit such a volume of data. However, they generally work at very high frequencies (typically 0.9 to 3 GHz). As a result, transmission distances can be very short, and usually, a line of sight is required between the transmitter and receiver. Therefore, they are not of use where the line of sight can be constantly interrupted by traffic and other such obstructions.
The present invention addresses the above problems and, in a first aspect provides, an apparatus for detecting a leak in a pipe, the apparatus comprising: a sensor located at a pipe configured to detect a signal from the pipe; converting means to convert the signal detected by the sensor into a digital signal; transform means to transform the digital signal into a different orthogonal space; and a transmitter for transmitting the transformed digital signal back to a remote processor.
Transforms which transform the digital data to a different orthogonal space are of particular use in processing acoustic signals from leaks. The transform should form an unconditional basis for the information. An unconditional basis results in expansion coefficients of a largely low order with a magnitude which decreases rapidly with increasing order.
The widely accepted definition of an unconditional basis follows loosely the form developed by Donoho in 1993 (D. L. Donoho, xe2x80x9cUnconditional Bases Are Optimal Basis For Data Compression And For Statistical Estimationxe2x80x9d, Applied and Computational Harmonic Analysis, I (1): 100-115, December 1993). An unconditional basis is formally defined by considering a function class F with a norm defined and denoted by ∥.∥F and a basis set fk such that any function gxcex5F has a unique representation g=xcexa3kakfk with equality defined as a limit using the norm, we consider the infinite expansion:       f    ⁡          (      t      )        =                    ∑                  k          =                      -            ∞                          ∞            ⁢              xe2x80x83            ⁢                        c          k                ⁢                  ϕ          ⁡                      (                          t              -              k                        )                                +                  ∑                  k          =                      -            ∞                          ∞            ⁢                        ∑                      j            =            0                    ∞                ⁢                  xe2x80x83                ⁢                              d                          j              ,              k                                ⁢                      ψ            ⁡                          (                                                                    2                    j                                    ⁢                  t                                -                k                            )                                          
if for all gxcex5F, the infinite sum converges for all |mk|xe2x89xa61, the basis is called an unconditional basis. Using such an unconditional basis, all subsequences of wavelets converge, and all sequences of subsequences converge. The convergence does not depend on the order of terms in the summation or on the sign of the coefficients. This implies a very robust basis, where the coefficients drop off rapidly for all members of the function class. This is the case for wavelets and leak noise data.
The transform is more preferably a discrete time wavelet transform of the form.       ψ    ⁡          (      t      )        =            ⅇ              -                  t          2                      ⁢          cos      ⁡              (                              π            ⁢                          xe2x80x83                        ·            t                    ⁢                                    2                              ln                ⁡                                  (                  2                  )                                                                    )            
Where f(t) represents the digital data outputted from the analogue to digital conversion means and t is the time. The xcfx86(txe2x88x92k) and "psgr"(2jtxe2x88x92k) functions represent the mother scaling functions and wavelet functions respectively which are used for the discrete wavelet transform.
The coefficients ck and dj,k are calculated from the inner product of f(t) with the scaling functions and wavelet functions such that:
ck=∫f(t)xcfx86(txe2x88x92k)dt
dj,k=∫f(t)"psgr"(2jtxe2x88x92k)dt
As the wavelet transforms used form an unconditional basis for the data, the magnitude of expansion coefficients (ck and dj,k above) drop off rapidly with increasing j and k. Therefore, large numbers of the coefficient array elements are very small or zero. The coefficients are preferably calculated using a fast transform technique. In common with the classic fast Fourier transform, the technique employed in this algorithm preferably requires that the number of data points in a packet is an exact power of 2.
Preferably, the discrete wavelet transform is a Fourier transform or a wavelet which encompasses a harmonic form. For example, a Morlet wavelet which is essentially a harmonic waveform modulated by a Gaussian envelope. A generic form of a Morlet wavelet can be expressed as:       g    ⁡          (      t      )        =            ∑      k        ⁢                  m        k            ⁢              a        k            ⁢                        f          k                ⁡                  (          t          )                    
More simplified forms of wavelet transforms could also be used, for example the so-called Haar transform. It is well known to those skilled in the art that the choice mother scalar function is dependent on the wavelet function.
The use of the above transform allows a data efficient compression technique to be performed before the signal is transmitted. Many known methods of comparing the signal taken from two adjacent sensors require some sort of wavelet transform to be performed on the signal (e.g. a Fourier Transform which is a specific type of wavelet transform). This is typically performed at the remote processor. Therefore, by performing the transform at the pipe, a more efficient data compression technique can be achieved without actually requiring any more processing steps.
The xe2x80x9crawxe2x80x9d data can be examined at the remote processor by performing the inverse transform at the remote processor.
Preferably, the transformed signal is passed through scalar quantising means. More preferably, the quantising means is configured to optimise the number of bits, to minimise the information loss in the reconstructed datastream. In typical use, the scalar quantiser is configured such that the number of bits in the outputted data stream is two more than the number of significant bits in the raw unprocessed acoustic datastream.
For transmission, the data which is outputted either directly from the wavelet transform means or from the quantising means is preferably further compressed prior to transmission. Therefore, the apparatus preferably further comprises an encoder means for further compressing the signal prior to transmission.
Preferably, the encoder means is configured to perform a loss less data compression technique. Loss less data compression is a term which is well understood in the fields of sound and image compression etc. It is applied to the process of starting with a source of data, in digital form (either in the form of a data stream, or stored in a file) and creating a representation for the digital data, which uses fewer bits than the original. A loss less compression process is achieved when it is possible to fully recreate the original data from the representation, i.e. the compression is fully reversible.
Many types of data compression transform techniques for example hierarchical encoding such as Huffman coding, or MP3L2 (two level MPEG conversion) could be used.
Hierarchical coding is often given different names such as entropy coding, run length coding and SPIHT (set partitioning in hierarchical trees). Huffman coding is a type of hierarchical coding. The preferred compression technique is very similar to a method used in JPEG (Joint Photographic Experts Group). This uses a special form of Huffman coding. These encoding techniques are of particular use when coupled to the output of a transform means of the type described above, since the transform means produces coefficients in the detailed region of the signal which have very low values.
Thus, the encoder means is preferably configured to comprise the steps of:
a) converting digitised data into an array of numbers, each number being assigned an index;
b) determining a first threshold value;
c) locating the last number in the array which is greater than the said threshold value;
d) adding the index of the said last number onto the data stream to be transmitted;
e) adding the numbers of the array up to and including the said last number, wherein numbers below the threshold being, added as a 0 and numbers above the threshold being added as a bit number with the sign of the number being encoded in the last bit;
f) setting a second threshold value wherein said second threshold is less than the first threshold; and
g) repeating steps c) to f) until a predetermined final threshold is reached.
Typically, steps c) to f) will be repeated as many times as required.
As previously mentioned, the apparatus of the first aspect of the present invention is primarily intended for use in a leak noise correlator, such a correlator will preferably comprise a plurality of sensors located on a pipe, each sensor comprising converting means to convert the signal detected by the sensor into a digital signal and transform means for transforming the digital signal into a different orthogonal space. A transmitter being provided for transmitting the transformed digital signal back to a remote processor. The remote processor being configured to compare the data received from adjacent sensors. A single transmitter may be shared between two or more sensors, or each sensor may have its own transmitter. The transmitter will generally be capable of transmitting a wireless signal.
In a preferred arrangement, the apparatus comprises both transform means and encoder means as previously described.
The remote processor may be a fixed base station. Alternatively, the remote processor could be mobile. In order to detect a leak, the mobile remote processor would have to detect a signal from at least two adjacent transmitters.
More preferably, the sensor and converter means are located in a module which is located at the pipe. The housing may be physically connected to the pipe, for example, by a magnet. The sensor may be located on the pipe or even within the pipe. For example, the sensor may be a hydrophone which extends from the module into the fluid stream of the pipe. Alternatively, the sensor may be mechanically connected to the pipe and configured to sense vibrations of the pipe itself.
A particularly preferable type of sensor is the accelerometer type of sensor. More preferably, the sensor comprises a compressible member wherein compression and expansion of the member causes an electric signal to be generated, the member being at least partially compressed when located in position at the pipe.
This may be achieved by providing the sensor in a sensor module, and providing a biasing member which is configured to bias the member against a rigid part of the sensor module. In a particularly preferred embodiment, the sensor module is provided with a compressible sealing member and said sealing member is used to bias said compressible member.
This pre-biasing of the compressible member minimises the sensitivity of the member to airborne ambient noise.
The signal from the sensor must be transmitted back to the remote processor. As the sensors will usually be located underground, the transmitter is preferably located within a box above or near the surface of the ground. The box, the sensor and the associated signal processing equipment provided between the box and the sensor form, what is called, an outstation.
Connection between the sensor and the box is usually achieved by a cable between the sensor and the box. Previously, the sensor used to transmit analogue data to the box. However, this seriously affected the signal quality which was then received by the transmitter. Therefore, preferably, at least the analogue to digital converter is located with the sensor such that digital data is communicated to the box containing the transmitter. The transform means and possibly the encoder means and/or quantising means may also be located at the sensor module such that their output is sent along the cable to the transmitter. Each sensor may have its own transmitter, or a transmitter may be configured to transmit the data from two or more sensors.
In a second aspect, the present invention provides a method of detecting a leak in a pipe, the method comprising the steps of detecting a signal from a pipe using a sensor located at the said pipe; converting the signal detected by the sensor into a digital signal; transforming the digital signal into a different orthogonal space; and transmitting the transformed digital signal. Preferably, the digital signal is transmitted back to a remote processor.